A Mathematical Model of TCR-T Cell Therapy for Cervical Cancer.

Engineered T cell receptor (TCR)-expressing T (TCR-T) cells are intended to drive strong anti-tumor responses upon recognition of the specific cancer antigen, resulting in rapid expansion in the number of TCR-T cells and enhanced cytotoxic functions, causing cancer cell death. However, although TCR-T cell therapy against cancers has shown promising results, it remains difficult to predict which patients will benefit from such therapy. We develop a mathematical model to identify mechanisms associated with an insufficient response in a mouse cancer model. We consider a dynamical system that follows the population of cancer cells, effector TCR-T cells, regulatory T cells (Tregs), and "non-cancer-killing" TCR-T cells. We demonstrate that the majority of TCR-T cells within the tumor are "non-cancer-killing" TCR-T cells, such as exhausted cells, which contribute little or no direct cytotoxicity in the tumor microenvironment (TME). We also establish two important factors influencing tumor regression: the reversal of the immunosuppressive TME following depletion of Tregs, and the increased number of effector TCR-T cells with antitumor activity. Using mathematical modeling, we show that certain parameters, such as increasing the cytotoxicity of effector TCR-T cells and modifying the number of TCR-T cells, play important roles in determining outcomes.

Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

Modeling the Development of Cellular Exhaustion and Tumor-Immune Stalemate.

Cellular exhaustion in various immune cells develops in response to prolonged stimulation and overactivation during chronic infections and in cancer. Marked by an upregulation of inhibitory receptors and diminished effector functions, exhausted immune cells are unable to fully eradicate the antigen responsible for the overexposure. In cancer settings, this results in a relatively small but constant tumor burden known as a localized tumor-immune stalemate. In recent years, studies have elucidated key aspects of the development and progression of cellular exhaustion and have re-addressed previous misconceptions. Biological publications have also provided insight into the functional capabilities of exhausted cells. Complementing these findings, the model presented here serves as a mathematical framework for the establishment of cellular exhaustion and the development of the localized stalemate against a solid tumor. Analysis of this model indicates that this stalemate is stable and can handle small perturbations. Additionally, model analysis also provides insight into potential targets of future immunotherapy efforts.

The Effectiveness of Case-management Rehabilitation Intervention in Facilitating Return to Work and Maintenance of Employment After Myocardial Infarction: Results of a Randomized Controlled Trial.

OBJECTIVE: To study the long-term effectiveness of case-management rehabilitation intervention on vocational reintegration of patients after myocardial infarction (MI). DESIGN: Blinded simple randomization was used to construct an intervention and control groups that were followed up for two years. SUBJECTS AND SETTING: 151 patients, aged 50.3 ± 5.9 years, who experienced uncomplicated MI and were enrolled in a cardiac rehabilitation program were recruited. INTERVENTIONS: included an early referral to an occupational physician, tailoring an occupational rehabilitation program, based on individual patient needs, coordination with relevant parties, psychosocial intervention, intensive follow-up sessions during a two-year follow-up. MAIN MEASURES: Return to work within six months of hospitalization and maintenance of employment at one and two years of follow-up. RESULTS: Return-to-work (RTW) rate in the intervention group was 89% and nearly all maintained employment at one year of follow-up (92%) and two years of follow-up (87%). Moreover, almost all of them returned to and maintained their previous jobs. The corresponding figures were: 98%, 94% and 98%, respectively. The figures for the RTW and employment maintenance for the control group were: 74%, 75%, and 72%, respectively. Only about 75%, in this group kept their previous job. The case-management intervention was associated with increased odds of maintaining employment at follow-up of one year (OR = 5.89, 95% CI 1.42-24.30) and two years (OR = 3.12, 95% CI 1.01-10.03). CONCLUSIONS: The extended case-management rehabilitation intervention had a substantial positive impact on both the RTW of MI patients and their maintenance of employment at one and two years of follow-up. TRIAL REGISTRATION: This trial is registered at US National Institutes of Health #NCT04934735.

Mining Marine Metagenomes Revealed a Quorum-Quenching Lactonase with Improved Biochemical Properties That Inhibits the Food Spoilage Bacterium Pseudomonas fluorescens.

The marine environment presents great potential as a source of microorganisms that possess novel enzymes with unique activities and biochemical properties. Examples of such are the quorum-quenching (QQ) enzymes that hydrolyze bacterial quorum-sensing (QS) signaling molecules, such as N-acyl-homoserine lactones (AHLs). QS is a form of cell-to-cell communication that enables bacteria to synchronize gene expression in correlation with population density. Searching marine metagenomes for sequences homologous to an AHL lactonase from the phosphotriesterase-like lactonase (PLL) family, we identified new putative AHL lactonases (sharing 30 to 40% amino acid identity to a thermostable PLL member). Phylogenetic analysis indicated that these putative AHL lactonases comprise a new clade of marine enzymes in the PLL family. Following recombinant expression and purification, we verified the AHL lactonase activity for one of these proteins, named moLRP (marine-originated lactonase-related protein). This enzyme presented greater activity and stability at a broad range of temperatures and pH, tolerance to high salinity levels (up to 5 M NaCl), and higher durability in bacterial culture, compared to another PLL member, parathion hydrolase (PPH). The addition of purified moLRP to cultures of Pseudomonas fluorescens inhibited its extracellular protease activity, expression of the protease encoding gene, biofilm formation, and the sedimentation process in milk-based medium. These findings suggest that moLRP is adapted to the marine environment and can potentially serve as an effective QQ enzyme, inhibiting the QS process in Gram-negative bacteria involved in food spoilage. IMPORTANCE Our results emphasize the potential of sequence and structure-based identification of new QQ enzymes from environmental metagenomes, such as from the ocean, with improved stability or activity. The findings also suggest that purified QQ enzymes can present new strategies against food spoilage, in addition to their recognized involvement in inhibiting bacterial pathogen virulence factors. Future studies on the delivery and safety of enzymatic QQ strategy against bacterial food spoilage should be performed.

Dysnumeria in Sign Language: Impaired Construction of the Decimal Structure in Reading Multidigit Numbers in a Deaf ISL Signer.

We report on the first in-depth analysis of a specific type of dysnumeria, number-reading deficit, in sign language. The participant, Nomi, is a 45-year-old signer of Israeli Sign Language (ISL). In reading multidigit numbers (reading-then-signing written numbers, the counterpart of reading aloud in spoken language), Nomi made mainly decimal, number-structure errors- reading the correct digits in an incorrect (smaller) decimal class, mainly in longer numbers of 5-6-digits. A unique property of ISL allowed us to rule out the numeric-visual analysis as the source of Nomi's dysnumeria: In ISL, when the multidigit number signifies the number of objects, it is signed with a decimal structure, which is marked morphologically (e.g., 84 → Eight-Tens Four); but a parallel system exists (e.g., for height, age, bus numbers), in which multidigit numbers are signed non-decimally, as a sequence of number-signs (e.g., 84 → Eight, Four). When Nomi read and signed the exact same numbers, but this time non-decimally, she performed significantly better. Additional tests supported the conclusion that her early numeric-visual abilities are intact: she showed flawless detection of differences in length, digit-order, or identity in same-different tasks. Her decimal errors did not result from a number-structure deficit in the phonological-sign output either (no decimal errors in repeating the same numbers, nor in signing multidigit numbers written as Hebrew words). Nomi had similar errors of conversion to the decimal structure in number comprehension (number-size comparison tasks), suggesting that her deficit is in a component shared by reading and comprehension. We also compared Nomi's number reading to her reading and signing of 406 Hebrew words. Nomi's word reading was in the high range of the normal performance of hearing controls and of deaf signers and significantly better than her multidigit number reading, demonstrating a dissociation between number reading, which was impaired, and word reading, which was spared. These results point to a specific type of dysnumeria in the number-frame generation for written multidigit numbers, whereby the conversion from written multidigit numbers to the abstract decimal structure is impaired, affecting both reading and comprehension. The results support abstract, non-verbal decimal structure generation that is shared by reading and comprehension, and also suggest the existence of a non-decimal number-reading route.

Modeling LSD1-Mediated Tumor Stagnation.

LSD1 (KDMA1) has gained attention in the last decade as a cancer biomarker and drug target. In particular, recent work suggests that LSD1 inhibition alone reduces tumor growth, increases T cell tumor infiltration, and complements PD1/PDL1 checkpoint inhibitor therapy. In order to elucidate the immunogenic effects of LSD1 inhibition, we develop a mathematical model of tumor growth under the influence of the adaptive immune response. In particular, we investigate the anti-tumor cytotoxicity of LSD1-mediated T cell dynamics, in order to better understand the synergistic potential of LSD1 inhibition in combination immunotherapies, including checkpoint inhibitors. To that end, we formulate a non-spatial delay differential equation model and fit to the B16 mouse model data from Sheng et al. (Cell 174(3):549-563, 2018. https://doi.org/10.1016/j.cell.2018.05.052 ). Our results suggest that the immunogenic effect of LSD1 inhibition accelerates anti-tumor cytotoxicity. However, cytotoxicity does not seem to account for the slower growth observed in LSD1-inhibited tumors, despite evidence suggesting immune-mediation of this effect.

Modeling the Effect of Memory in the Adaptive Immune Response.

It is well understood that there are key differences between a primary immune response and subsequent responses. Specifically, memory T cells that remain after a primary response drive the clearance of antigen in later encounters. While the existence of memory T cells is widely accepted, the specific mechanisms that govern their function are generally debated. In this paper, we develop a mathematical model of the immune response. This model follows the creation, activation, and regulation of memory T cells, which allows us to explore the differences between the primary and secondary immune responses. Through the incorporation of memory T cells, we demonstrate how the immune system can mount a faster and more effective secondary response. This mathematical model provides a quantitative framework for studying chronic infections and auto-immune diseases.

Study of dose-dependent combination immunotherapy using engineered T cells and IL-2 in cervical cancer.

Adoptive T cell based immunotherapy is gaining significant traction in cancer treatment. Despite its limited efficacy so far in treating solid tumors compared to hematologic cancers, recent advances in T cell engineering render this treatment increasingly more successful in solid tumors, demonstrating its broader therapeutic potential. In this paper we develop a mathematical model to study the efficacy of engineered T cell receptor (TCR) T cell therapy targeting the E7 antigen in cervical cancer cell lines. We consider a dynamical system that follows the population of cancer cells, TCR T cells, and IL-2 treatment concentration. We demonstrate that there exists a TCR T cell dosage window for a successful cancer elimination that can be expressed in terms of the initial tumor size. We obtain the TCR T cell dose for two cervical cancer cell lines: 4050 and CaSki. Finally, a combination therapy of TCR T cell and IL-2 treatment is studied. We show that certain treatment protocols can improve therapy responses in the 4050 cell line, but not in the CaSki cell line.

Universal response in the RKO colon cancer cell line to distinct antimitotic therapies.

Both classic and newer antimitotics commonly induce a prolonged mitotic arrest in cell culture. During arrest, cells predominantly undergo one of two fates: cell death by apoptosis, or mitotic slippage and survival. To refine this binary description, a quantitative understanding of these cell responses is needed. Herein, we propose a quantitative description of the kinetics of colon carcinoma RKO cell fates in response to different antimitotics, using data from the single cell experiments of Gascoigne and Taylor (2008). The mathematical model is calibrated using the in vitro experiments of Gascoigne and Taylor (2008). We show that the time-dependent probability of cell death or slippage is universally identical for monastrol, nocodazole and two different doses of AZ138, but significantly different for taxol. Death and slippage responses across drugs can be characterized by Gamma distributions. We demonstrate numerically that these rates increase with prolonged mitotic arrest. Our model demonstrates that RKO cells exhibit a triphasic response - first, remain in mitosis, then undergo fast and slow transition, respectively- dependent on the length of mitotic arrest and irrespective of cell fate, drug type or dose.

Stability Analysis of a Model of Interaction Between the Immune System and Cancer Cells in Chronic Myelogenous Leukemia.

We describe here a simple model for the interaction between leukemic cells and the autologous immune response in chronic phase chronic myelogenous leukemia (CML). This model is a simplified version of the model we proposed in Clapp et al. (Cancer Res 75:4053-4062, 2015). Our simplification is based on the observation that certain key characteristics of the dynamics of CML can be captured with a three-compartment model: two for the leukemic cells (stem cells and mature cells) and one for the immune response. We characterize the existence of steady states and their stability for generic forms of immunosuppressive effects of leukemic cells. We provide a complete co-dimension one bifurcation analysis. Our results show how clinical response to tyrosine kinase inhibitors treatment is compatible with the existence of a stable low disease, treatment-free steady state.

Modeling the Dynamics of Heterogeneity of Solid Tumors in Response to Chemotherapy.

In this paper, we extend the model of the dynamics of drug resistance in a solid tumor that was introduced by Lorz et al. (Bull Math Biol 77:1-22, 2015). Similarly to the original, radially symmetric model, the quantities we follow depend on a phenotype variable that corresponds to the level of drug resistance. The original model is modified in three ways: (i) We consider a more general growth term that takes into account the sensitivity of resistance level to high drug dosage. (ii) We add a diffusion term in space for the cancer cells and adjust all diffusion terms (for the nutrients and for the drugs) so that the permeability of the resource and drug is limited by the cell concentration. (iii) We add a mutation term with a mutation kernel that corresponds to mutations that occur regularly or rarely. We study the dynamics of the emerging resistance of the cancer cells under continuous infusion and on-off infusion of cytotoxic and cytostatic drugs. While the original Lorz model has an asymptotic profile in which the cancer cells are either fully resistant or fully sensitive, our model allows the emergence of partial resistance levels. We show that increased drug concentrations are correlated with delayed relapse. However, when the cancer relapses, more resistant traits are selected. We further show that an on-off drug infusion also selects for more resistant traits when compared with a continuous drug infusion of identical total drug concentrations. Under certain conditions, our model predicts the emergence of a heterogeneous tumor in which cancer cells of different resistance levels coexist in different areas in space.

Modeling Cancer Cell Growth Dynamics In vitro in Response to Antimitotic Drug Treatment.

Investigating the role of intrinsic cell heterogeneity emerging from variations in cell-cycle parameters and apoptosis is a crucial step toward better informing drug administration. Antimitotic agents, widely used in chemotherapy, target exclusively proliferative cells and commonly induce a prolonged mitotic arrest followed by cell death via apoptosis. In this paper, we developed a physiologically motivated mathematical framework for describing cancer cell growth dynamics that incorporates the intrinsic heterogeneity in the time individual cells spend in the cell-cycle and apoptosis process. More precisely, our model comprises two age-structured partial differential equations for the proliferative and apoptotic cell compartments and one ordinary differential equation for the quiescent compartment. To reflect the intrinsic cell heterogeneity that governs the growth dynamics, proliferative and apoptotic cells are structured in "age," i.e., the amount of time remaining to be spent in each respective compartment. In our model, we considered an antimitotic drug whose effect on the cellular dynamics is to induce mitotic arrest, extending the average cell-cycle length. The prolonged mitotic arrest induced by the drug can trigger apoptosis if the time a cell will spend in the cell cycle is greater than the mitotic arrest threshold. We studied the drug's effect on the long-term cancer cell growth dynamics using different durations of prolonged mitotic arrest induced by the drug. Our numerical simulations suggest that at confluence and in the absence of the drug, quiescence is the long-term asymptotic behavior emerging from the cancer cell growth dynamics. This pattern is maintained in the presence of small increases in the average cell-cycle length. However, intermediate increases in cell-cycle length markedly decrease the total number of cells and can drive the cancer population to extinction. Intriguingly, a large "switch-on/switch-off" increase in the average cell-cycle length maintains an active cell population in the long term, with oscillating numbers of proliferative cells and a relatively constant quiescent cell number.

Modeling the Transfer of Drug Resistance in Solid Tumors.

ABC efflux transporters are a key factor leading to multidrug resistance in cancer. Overexpression of these transporters significantly decreases the efficacy of anti-cancer drugs. Along with selection and induction, drug resistance may be transferred between cells, which is the focus of this paper. Specifically, we consider the intercellular transfer of P-glycoprotein (P-gp), a well-known ABC transporter that was shown to confer resistance to many common chemotherapeutic drugs. In a recent paper, Durán et al. (Bull Math Biol 78(6):1218-1237, 2016) studied the dynamics of mixed cultures of resistant and sensitive NCI-H460 (human non-small lung cancer) cell lines. As expected, the experimental data showed a gradual increase in the percentage of resistance cells and a decrease in the percentage of sensitive cells. The experimental work was accompanied with a mathematical model that assumed P-gp transfer from resistant cells to sensitive cells, rendering them temporarily resistant. The mathematical model provided a reasonable fit to the experimental data. In this paper, we develop a new mathematical model for the transfer of drug resistance between cancer cells. Our model is based on incorporating a resistance phenotype into a model of cancer growth (Greene et al. in J Theor Biol 367:262-277, 2015). The resulting model for P-gp transfer, written as a system of integro-differential equations, follows the dynamics of proliferating, quiescent, and apoptotic cells, with a varying resistance phenotype. We show that this model provides a good match to the dynamics of the experimental data of Durán et al. (2016). The mathematical model shows a better fit when resistant cancer cells have a slower division rate than the sensitive cells.

BCR-ABL transcript variations in chronic phase chronic myelogenous leukemia patients on imatinib first-line: Possible role of the autologous immune system.

Many chronic myelogenous leukemia (CML) patients in chronic phase who respond well to imatinib therapy show fluctuations in their leukemic loads in the long-term. We developed a mathematical model of CML that incorporates the intervention of an autologous immune response. Our results suggest that the patient's immune system plays a crucial role in imatinib therapy in maintaining disease control over time. The observed BCR-ABL/ABL oscillations in such patients provide a signature of the autologous immune response.

Modeling the Dynamics of High-Grade Serous Ovarian Cancer Progression for Transvaginal Ultrasound-Based Screening and Early Detection.

High-grade serous ovarian cancer (HGSOC) represents the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. A timely detection of low volume HGSOC should be the goal of any screening studies. However, numerous transvaginal ultrasound (TVU) detection-based population studies aimed at detecting low-volume disease have not yielded reduced mortality rates. A quantitative invalidation of TVU as an effective HGSOC screening strategy is a necessary next step. Herein, we propose a mathematical model for a quantitative explanation on the reported failure of TVU-based screening to improve HGSOC low-volume detectability and overall survival.We develop a novel in silico mathematical assessment of the efficacy of a unimodal TVU monitoring regimen as a strategy aimed at detecting low-volume HGSOC in cancer-positive cases, defined as cases for which the inception of the first malignant cell has already occurred. Our findings show that the median window of opportunity interval length for TVU monitoring and HGSOC detection is approximately 1.76 years. This does not translate into reduced mortality levels or improved detection accuracy in an in silico cohort across multiple TVU monitoring frequencies or detection sensitivities. We demonstrate that even a semiannual, unimodal TVU monitoring protocol is expected to miss detectable HGSOC. Lastly, we find that circa 50% of the simulated HGSOC growth curves never reach the baseline detectability threshold, and that on average, 5-7 infrequent, rate-limiting stochastic changes in the growth parameters are associated with reaching HGSOC detectability and mortality thresholds respectively. Focusing on a malignancy poorly studied in the mathematical oncology community, our model captures the dynamic, temporal evolution of HGSOC progression. Our mathematical model is consistent with recent case reports and prospective TVU screening population studies, and provides support to the empirical recommendation against frequent HGSOC screening.

Mathematical models of breast and ovarian cancers.

Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review, we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, as answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions. WIREs Syst Biol Med 2016, 8:337-362. doi: 10.1002/wsbm.1343 For further resources related to this article, please visit the WIREs website.

Mathematical Modeling Reveals That Changes to Local Cell Density Dynamically Modulate Baseline Variations in Cell Growth and Drug Response.

Cell-to-cell variations contribute to drug resistance with consequent therapy failure in cancer. Experimental techniques have been developed to monitor tumor heterogeneity, but estimates of cell-to-cell variation typically fail to account for the expected spatiotemporal variations during the cell growth process. To fully capture the extent of such dynamic variations, we developed a mechanistic mathematical model supported by in vitro experiments with an ovarian cancer cell line. We introduce the notion of dynamic baseline cell-to-cell variation, showing how the emerging spatiotemporal heterogeneity of one cell population can be attributed to differences in local cell density and cell cycle. Manipulation of the geometric arrangement and spatial density of cancer cells revealed that given a fixed global cell density, significant differences in growth, proliferation, and paclitaxel-induced apoptosis rates were observed based solely on cell movement and local conditions. We conclude that any statistical estimate of changes in the level of heterogeneity should be integrated with the dynamics and spatial effects of the baseline system. This approach incorporates experimental and theoretical methods to systematically analyze biologic phenomena and merits consideration as an underlying reference model for cell biology studies that investigate dynamic processes affecting cancer cell behavior. Cancer Res; 76(10); 2882-90. ©2016 AACR.

[THE IMPACT OF A COMMUNITY INTERVENTION PROGRAM IN ISRAELI WORKSITES TO REDUCE THE RISK OF CARDIOVASCULAR DISEASES].

INTRODUCTION: Atherosclerosis is the main cause of cardiovascular (CV) morbidity and mortality in the western world. Detection and treatment of risk factors (such as hypertension, dyslipidemia and diabetes mellitus) reduce CV events. We have shown cost utility in reducing these CV risk factors in community clinics and community centers. AIMS: In this paper we focused on community workplaces. METHODS: We included 1011 workers in 15 worksites in the study. All workers were analyzed for CV risk factors and included in 6 months of intervention in their worksite in order to reduce the burden of CV risk factors. RESULTS: Significant reduction was noted in the percentage of high risk patients from 43.7% to 27.8% to 24.7% after 3 and 6 months respectively (p<0.05), in the percentage of workers with high weight circumference, high blood pressure, smokers, and in high body mass index (>30g/m2 ). CONCLUSIONS: Interventions in community workplaces can improve the CV risk factors profile and thus should be implemented as broadly as possible.